Urbanization represents one of the most pervasive forces of anthropogenic change over the last century. More than 50% of the human population now occupies urban areas, and most ecosystems will experience urbanization in the near future. Remnant urban habitat patches often contain extremely high population densities of just a few urban adapters or exploiters, leading to biological homogenization. Urban adapters achieve high population densities due to artificially high primary productivity, less severe temperature fluctuations due to the heat island effect, a more stable and abundant food supply from human supplementation, and release from trophic competition in urban environments. Isolated urban populations experiencing the ecological conditions described above may rapidly adapt to local conditions, but few studies have examined this possibility. The emerging field of population genomics uses computational approaches to identify statistical outliers among large numbers of loci that are under selection. Recently, the advent of next generation sequencing has made it possible to generate millions of sequences relatively cheaply and quickly, thus vastly improving the power to detect single nucleotide polymorphisms (SNPs) indicative of local adaptation. We will use these new approaches to examine local adaptation to urbanization among isolated populations of an urban adapter in New York City, the white-footed mouse (Peromyscus leucopus). First, we will generate deep transcriptome sequence for white-footed mice from urban and non-urban populations using 454 pyrosequencing. Gene identity will be established through alignment with annotated rat, mouse, and Peromyscus genomes (Aim 1). We will then identify coding sequences that exhibit statistical signatures of selection at non-synonymous SNPs in urban populations. We will examine these regions in multiple populations to examine whether selection pressures from urbanization result in an evolutionary syndrome of correlated change across the landscape (Aim 2). It is difficult to predict a priori which genes will be under selection, but loci that facilitate dealing with physiological stress from high intraspecific competition, disease, and chronic exposure to pollutants are likely examples. Next, we will examine additional genomic variation between urban vs. non-urban populations due to alternative splicing and differential expression. We will use RNA-seq methodology on the Illumina next-generation sequencing platform to identify alternative splice variants and quantify transcript abundance across populations (Aim 3).